Book Cover

Redefining Independent Learning with AI Agents

Contributor(s): Shaheen, Momina (Editor), Pandey, Jay Kumar (Editor), Ahmad, Faizan (Editor), Rasheed, Jawad (Editor), Khan, Maqbool (Editor)

ISBN: 9798337372259

Publisher: Igi Global Scientific Publishing

Hardcover
$185.00
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Pub Date: August 14, 2026

Lexile Code: 0000

Target Age Group: NA to NA

Physical Info: 1.25" H x 11.00" L x 8.50" W ( 3.60 lbs) 500 pages

Descriptions, Reviews, etc.

Description: The advancement of AI transforms how individuals approach learning, giving rise to a new model of independence supported by intelligent tools. AI agents can personalize instruction, provide real-time feedback, and adapt to learners' needs, enabling more self-directed and flexible educational experiences. As traditional instruction changes, AI-driven systems reshape the role of guidance, motivation, and assessment. Understanding this shift is essential for evaluating both the opportunities and challenges of independent learning in a digital world. Redefining Independent Learning With AI Agents investigates how AI agents redefine education and independent learning by offering targeted, adaptive, and scalable solutions. It explores how AI-powered tools can raise participation, enhance accessibility, and promote lifelong learning. This book covers topics such as adaptive learning, digital technology, and education assessment, and is a useful resource for engineers, educators, academicians, researchers, and scientists.

Brief description: Momina Shaheen is an academic and researcher in computer science with over 8 years of experience in teaching and research. She currently serves as a Lecturer in Computing and Programme Leader at the University of Roehampton London. Her work focuses on edge computing, the Internet of Things, federated learning, and cybersecurity, with applications across smart cities, healthcare, finance, and education. Shaheen holds a B.Sc. in Information Technology, an M.Eng. in Software Engineering, and defended her Ph.D. in Computer Science, focusing on improving deep learning performance in federated machine learning. Her academic contributions include over 46 peer-reviewed publications in Q1 journals, multiple book chapters, and editorial work, earning her an h-index of 9 and over 400 citations. She has taught a wide range of undergraduate and postgraduate modules, including algorithms, software development, artificial intelligence, and data visualization. In addition to her teaching, she leads international collaborations and actively contributes to curriculum design, grant writing, and supervision of research projects. She also served as a reviewer for prominent journals such as Nature (under Early Career Reviewer Program), MDPI Electronics, IEEE Access, IET Information Security, Springer Scientific Reports, PLOS ONE, and ACM Transactions, and has chaired international conferences. Shaheen is committed to fostering innovation in computing education, and intelligent systems.

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